Location Data Histogram for User Interest Inference
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Solution Overview
Problem
Current location-based services struggle to accurately infer user interests and provide personalized recommendations based on geographic location data, often relying on manual input or incomplete data analysis.
Innovation Solution
A method and system that processes location data points to generate visited place data, building a histogram across categories of locations visited, and using this data to serve recommendations by determining user interests and preferences, with features like clustering, local searches, and query log analysis to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If location data is collected and analyzed to infer user interests, then recommendation accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments location data into discrete data points with specific attributes (latitude, longitude, timestamp, category). By dividing continuous location information into structured segments, the system can efficiently process and analyze user movement patterns without being overwhelmed by raw data volume, thus improving recommendation accuracy while managing processing complexity.
Solution Approach 2:
The patent adds temporal dimension by incorporating timestamps with location data, transforming spatial-only information into spatio-temporal data. This dimensional enhancement enables the system to analyze not just where users go but when they go, significantly improving interest inference accuracy by capturing usage patterns and preferences over time.
2Loss of information
If comprehensive location data is processed to build histograms across categories, then user interest inference is improved, but computational time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing location data into structured data points with categories and timestamps, and pre-building histograms across different location categories. This preparation work is done in advance so that when recommendations are needed, the system can quickly query pre-computed statistics rather than processing raw data from scratch, reducing computational time while maintaining inference accuracy.
Solution Approach 2:
The patent applies partial action by focusing histogram building on specific location categories rather than processing all possible data uniformly. By selectively analyzing relevant categories based on user behavior patterns, the system achieves sufficient inference accuracy without the excessive computational cost of processing every possible data point and category in detail.
3Productivity
If location patterns are analyzed to serve personalized recommendations, then user engagement is improved, but system resource consumption increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically analyze location patterns and generate recommendations without requiring manual user input or configuration. The system autonomously processes location data, builds histograms, infers user interests, and serves personalized recommendations, reducing the need for human intervention and associated resource consumption while maintaining high user engagement through relevant suggestions.
Data Source
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AI summary
Techniques are disclosed herein for inferring user interests and serving recommendations to the user. An example for serving recommendations based on inferred user interests is described. The example includes receiving a plurality of location data points for a user, the location data points including a geographic position and a time, and storing the location data points in a data store. The location data points are processed using a processor in communication with the data store to generate visited place data for the user where the visited place data represents a location visited by the user, a category of the location visited, and a time. A histogram of the visited place data is built across categories of locations visited and the histogram is stored in the data store. Recommendations are then served to the user within a category represented on the histogram.